This module emphasizes how the theory and application of transcriptomics can be extended to include other types of omic analysis, and then integrated using statistical and machine learning tools. It starts with the statistical basis of hypothesis testing, covering the central role of normalization strategies and the specifics of differential expression analysis. The module then discusses options for downstream processing by clustering and module comparison; extensions to methylation profiling, proteomics, and metabolomics; eQTL analysis including fine mapping of regulatory variation; and integrative methodologies addressing the relationship between genomic, meta-genomic, and phenotypic variation. The module will also emphasize the landscape of computational problems in single-cell RNA sequencing, including clustering, trajectory inference, imputation, and data integration. This module deals primarily with upstream data processing methods that lead to the delineation of networks and pathways.
Suggested pairing: Module 16: Pathway and Network Analysis for Omics Data
Learning Objectives: After attending this module, participants will be able to:
- To see how good experimental design is central to being able to extract more inference from gene expression profiling experiments.
- To know the key features of the open source DESeq, edgeR, and Seurat packages that are commonly used for transcriptomics, while also learning about alternative options.
- To appreciate the importance of normalization strategies to avoid biases and maximize statistical power to detect biological effects.
- To understand the principles of eQTL analysis and the computational methods for dissecting regulatory mechanisms by integration with chromatin profiling data.
- To obtain a good understanding of the landscape of computational problems in single-cell RNA sequencing analysis
- To be prepared to integrate knowledge gained from this module into other Summer Institute modules such as Pathway Analysis, and Advanced Quantitative Genetics.